VIGE: virus-induced genome editing for improving abiotic and biotic stress traits in plants

Agricultural production is hampered by disease, pests, and environmental stresses. To minimize yield loss, it is important to develop crop cultivars with resistance or tolerance to their respective biotic and abiotic constraints. Transformation techniques are not optimized for many species and desirable cultivars may not be amenable to genetic transformation, necessitating inferior cultivar usage and time-consuming introgression through backcrossing to the preferred variety. Overcoming these limitations will greatly facilitate the development of disease, insect, and abiotic stress tolerant crops. One such avenue for rapid crop improvement is the development of viral systems to deliver CRISPR/Cas-based genome editing technology to plants to generate targeted beneficial mutations. Viral delivery of genomic editing constructs can theoretically be applied to span the entire host range of the virus utilized, circumventing the challenges associated with traditional transformation and breeding techniques. Here we explore the types of viruses that have been optimized for CRISPR/Cas9 delivery, the phenotypic outcomes achieved in recent studies, and discuss the future potential of this rapidly advancing technology.

Peculiarities of the Transformation of Asteraceae Family Species: The Cases of Sunflower and Lettuce

The Asteraceae family is the largest and most diversified family of the Angiosperms, characterized by the presence of numerous clustered inflorescences, which have the appearance of a single compound flower. It is estimated that this family represents around 10% of all flowered species, with a great biodiversity, covering all environments on the planet, except Antarctica. Also, it includes economically important crops, such as lettuce, sunflower, and chrysanthemum; wild flowers; herbs, and several species that produce molecules with pharmacological properties. Nevertheless, the biotechnological improvement of this family is limited to a few species and their genetic transformation was achieved later than in other plant families. Lettuce (Lactuca sativa L.) is a model species in molecular biology and plant biotechnology that has easily adapted to tissue culture, with efficient shoot regeneration from different tissues, organs, cells, and protoplasts. Due to this plasticity, it was possible to obtain transgenic plants tolerant to biotic or abiotic stresses as well as for the production of commercially interesting molecules (molecular farming). These advances, together with the complete sequencing of lettuce genome allowed the rapid adoption of gene editing using the CRISPR system. On the other hand, sunflower (Helianthus annuus L.) is a species that for years was considered recalcitrant to in vitro culture. Although this difficulty was overcome and some publications were made on sunflower genetic transformation, until now there is no transgenic variety commercialized or authorized for cultivation. In this article, we review similarities (such as avoiding the utilization of the CaMV35S promoter in transformation vectors) and differences (such as transformation efficiency) in the state of the art of genetic transformation techniques performed in these two species.

Automated Generation of Masked Hardware

Masking has been recognized as a sound and secure countermeasure for cryptographic implementations, protecting against physical side-channel attacks. Even though many different masking schemes have been presented over time, design and implementation of protected cryptographic Integrated Circuits (ICs) remains a challenging task. More specifically, correct and efficient implementation usually requires manual interactions accompanied by longstanding experience in hardware design and physical security. To this end, design and implementation of masked hardware often proves to be an error-prone task for engineers and practitioners. As a result, our novel tool for automated generation of masked hardware (AGEMA) allows even inexperienced engineers and hardware designers to create secure and efficient masked cryptograhic circuits originating from an unprotected design. More precisely, exploiting the concepts of Probe-Isolating Non-Interference (PINI) for secure composition of masked circuits, our tool provides various processing techniques to transform an unprotected design into a secure one, eventually accelerating and safeguarding the process of masking cryptographic hardware. Ultimately, we evaluate our tool in several case studies, emphasizing different trade-offs for the transformation techniques with respect to common performance metrics, such as latency, area, andrandomness.

Analysis and Transformation of Constrained Horn Clauses for Program Verification

This paper surveys recent work on applying analysis and transformation techniques that originate in the field of constraint logic programming (CLP) to the problem of verifying software systems. We present specialization-based techniques for translating verification problems for different programming languages, and in general software systems, into satisfiability problems for constrained Horn clauses (CHCs), a term that has become popular in the verification field to refer to CLP programs. Then, we describe static analysis techniques for CHCs that may be used for inferring relevant program properties, such as loop invariants. We also give an overview of some transformation techniques based on specialization and fold/unfold rules, which are useful for improving the effectiveness of CHC satisfiability tools. Finally, we discuss future developments in applying these techniques.

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

PurposeWeb users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.Design/methodology/approachThe proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.FindingsThe comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.Research limitations/implicationsThis study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.Practical implicationsThis study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.Social implicationsIn the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.Originality/valueThis study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Philos-Type Oscillation Results for Third-Order Differential Equation with Mixed Neutral Terms

The motivation for this paper is to create new Philos-type oscillation criteria that are established for third-order mixed neutral differential equations with distributed deviating arguments. The key idea of our approach is to use the triple of the Riccati transformation techniques and the integral averaging technique. The established criteria improve, simplify and complement results that have been published recently in the literature. An example is also given to demonstrate the applicability of the obtained conditions.

The practice of simplifying texts in the teaching of Polish as a foreign language (formerly and today)

The aim of the article is to show that the practice of simplifying texts, referring to the concept of plain language, has long been present in the teaching of Polish as a foreign language. Nowadays, various strategies of text simplification are used for teaching Polish, the most useful of which are: simulation of original texts with the use of graphic materials (especially at levels A1 and A2), text development (by exemplification or paraphrasing) and the technique of text glossing (in the target or intermediary language). An analysis of old textbooks for learning Polish shows that the practice of modifying teaching texts has also been applied in the past. The transformation techniques used formerly concern both the global structure of the text and changes at the word, sentence and paragraph level.

A Review of Flow-Capacitated Networks: Algorithms, Techniques and Applications

This paper presents a review of flow network concepts, including definition of some graph-theoretic basics and a discussion of network flow properties. It also provides an overview of some crucial algorithms used to solve the maximum-flow problem such as the Ford and Fulkerson algorithm (FFA), supplemented with alternative solutions, together with the essential terminology for this algorithm. Moreover, this paper explains the max-flow min-cut theorem in detail, analyzes the concepts behind it, and provides some examples and their solutions to demonstrate this theorem. As a bonus, it expounds the reduction and transformation techniques used in a capacitated network. In addition, this paper reviews one of the popular techniques for analyzing capacitated networks, which is the “decomposition technique”. This technique is centered on conditioning a complicated network on the possible states of a keystone element or on the possible combinations of states of many keystone elements. Some applications of capacitated network problems are addressed based on each type of problem being discussed.